1 Introduction

This R Markdown script contains all the code used for outlier detection, data analysis, and plotting. It includes the description for materials, methods, additional statistical analyses, Monte Carlo simulations, and all the statistical models with summaries.

2 Experiment 1

2.1 Stimuli

Exp1 (identification task) tests NMS’ implicit knowledge of Māori words with varying frequencies. The stimulus materials for the identification task consist of 1,000 Māori words and 1,000 Māori-like nonwords. To obtain a list of words, all words in two Māori running speech data (32, 33) are divided into five frequency bins and 200 words are randomly selected from each of those bins. A list of 10,000 pseudowords (1,000 pseudowords for each phoneme length of nonwords ranging from length 3 to length 12) were generated from a trigram language model by a pseudoword generator (34) using a Māori dictionary (35) and two running speech corpora (32, 33) as training sets. For each word, a nonword is chosen by matching its length and phonotactic score. The phonotactic scores of stimuli are computed using type frequency obtained from a Māori dictionary (35). A trigram model is selected in order to take positional information within words into account, including an additional symbol to indicate word boundaries while building a language model. The phonotactic score of each stimulus is obtained by summing log-transformed transitional probabilities which better reflect frequency distributions than raw probabilities (36) and then is normalized by length. The Kneser-Ney smoothing in SRILM (SRI Language Modeling Toolkit) (37) is used to deal with unknown trigrams in nonwords since nonwords are created by training data composed of two RS corpora (32, 33) and a Māori dictionary(35),some nonwords contain trigrams which are not observed in a trigram language model built from a dictionary. Every participant had a different list of 300 stimuli containing 30 pairs (words and nonwords) randomly selected from each of five bins (30*2*5 = 300 in total). The list of stimuli used in Exp1 can be found in “StimuliExp1.txt”.

2.2 Participants

In Exp1, nonfluent Māori speakers based in New Zealand (NMS), 18 years or older, were recruited online by Facebook Ads. Several exclusion criteria were applied to filter out unusable participants in this experiment. All participants speak New Zealand English as their first language. However, nine participants learned English outside New Zealand and among them, we discard one participant who currently lives in New Zealand for less than ten years. Furthermore, based on their answers to the post-questionnaire, two participants whose level of self-reported proficiency of Māori corresponds to at least “fairly well”, two participants with some knowledge of any other Polynesian languages, and five participants who reported a history of any speech or language impairments are removed. Due to a technical error during the experiment, the data of one participant who rated too small number of stimuli is discarded. After removing those outliers, we also look at the pattern of variability in participants’ responses and discard one participant whose standard deviation of ratings falls below two times the standard deviation below the mean among participants.

# Loading the data to filter out participants
dataExp1 <- read.delim("./dataNotFilteredExp1.txt", sep ="\t", header = TRUE)

# Remove one participant who did 192 items instead of 303
nbResponse <- aggregate(dataExp1$enteredResponse, by=list(dataExp1$workerId),  FUN=length)
rmParticipant1Exp1 <- nbResponse[!nbResponse$x %in% c(303),]$Group.1 # CEA6BA7F432A7DD9F3352BD5AF605FED
dataExp1 <- dataExp1[!dataExp1$workerId %in% rmParticipant1Exp1,]

# Remove three participants who gave ratings lower than 4 to 3 most common Māori words borrowed into New Zealand English
list_carrot <- c("haka","kai","aotearoa")
carrots <- dataExp1[dataExp1$word %in% list_carrot,]
carrots <- carrots[as.numeric(carrots$enteredResponse) < 4,]
rmParticipant2Exp1 <- unique(carrots$workerId) # C2172ACC5CE7A076DF4AD48672826D17 CF5B1FC9D9FDDE28DABFB8B10F60F2A8 F317F5E5817BC9CA03FFA3D0AEE15BCC
dataExp1 <- dataExp1[!dataExp1$workerId %in% rmParticipant2Exp1,]

# Remove ratings for the three words (used to detect outliers)
dataExp1 <- dataExp1[!dataExp1$word %in% c("haka","kai","aotearoa"),]

# Remove two participants whose speakMaori or compMaori is at least (equal to or above) 3
rmParticipant3Exp1 <- unique(dataExp1[dataExp1$speakMaori >= 3 | dataExp1$compMaori >= 3,]$workerId)  # 0CAEF0D07D102126BADE4B5F353F3549 FFF87DE6A3C8DCD73B120BD06C3EB966
dataExp1 <- dataExp1[!dataExp1$workerId %in% rmParticipant3Exp1,]

# Remove one participant who did not learn their English in NZ and have been living in their current location in NZ for less than ten years (duration == "short")
summaryExp1WorkerId <- unique(dataExp1[,c("workerId","firstLangCountry","place","duration")])
EngNotInNZExp1 <- summaryExp1WorkerId[!summaryExp1WorkerId$firstLangCountry=="NZ",]
rmParticipant4Exp1 <- unique(EngNotInNZExp1[EngNotInNZExp1$duration=="short",]$workerId) # 31F2039C03219AE69C49AC468F37CA78
dataExp1 <- dataExp1[!dataExp1$workerId %in% rmParticipant4Exp1,]

# Remove two participants who know any other Polynesian languages
rmParticipant5Exp1 <- unique(dataExp1[dataExp1$anyPolynesian=="Yes",]$workerId) # 2B474DC72567F38FD065757F11211DFD 2B92687E2CE8ABD8047732400DFDC424
dataExp1 <- dataExp1[!dataExp1$workerId %in% rmParticipant5Exp1,]

# Remove five participants with language impairments
rmParticipant6Exp1 <- unique(dataExp1[dataExp1$impairments=="Yes",]$workerId) 
# 1DD4DB335A0E2735A9AE2AD898AAEFD5 2C40866C3268DB45274C535937D3DA24 933DFF677472628574F9DB2874121FD9 D42298BA8985A7C8E6232CCFC8E99A31 F4599D280C20128B3F6E804C0D2726B0
dataExp1 <- dataExp1[!dataExp1$workerId %in% rmParticipant6Exp1,]

# Remove one participant whose pattern of responses (SD) is below 2SD of the mean of all participants
SD <- aggregate(dataExp1$enteredResponse, by=list(dataExp1$workerId), sd)
cut <- mean(SD$x)-2*sd(SD$x)
rmParticipant7Exp1 <- SD[!SD$x > cut,]$Group.1 # B855CE4A15710AA41194B3D81EE68187
dataExp1 <- dataExp1[!dataExp1$workerId %in% rmParticipant7Exp1,]

# Check the total number of usable participants for Exp1
# length(unique(dataExp1$workerId)) # 85

2.3 Overview of participants’ sociolinguistic profile

Regarding participants’ age and highest level of education, the distribution is quite balanced in comparison to their self-reported level of Māori proficiency, current place of living and gender. There are substantially more female participants than male and those living in the North Island than in the South Island. Regarding their sociolinguistic profile in relation to Māori, after filtering out participants who considered their level of Māori proficiency at least as fairly well, most participants self-reported that they had some basic knowledge of Māori and only a few participants responded that their level of exposure to Māori was less than once a year. This figure summarizes the distribution of participants on demographic and linguistic axes.

Fig. S1: Overview of participants’ sociolinguistic profile in Exp1.

2.4 Dataset structure

The data is structured as follows:

workerId is the unique ID for each participant.
enteredResponse is the wellformedness rating for each stimulus.
reactionTime is the reaction time for each rating (second).
type is the classification of each stimulus: word (‘real’) or nonword (‘pseudo’).
length is the phoneme length of each stimulus.
word is the stimulus used for the rating.
speakMaori is each participant’s answer to the question how well they can speak Māori (with a scale ranging from 0 to 5).
compMaori is each participant’s answer to the question how well they can understand/read Māori (with a scale ranging from 0 to 5).
maoriProf is the sum of quantified response for speakMaori and compMaori, which refers to the level of each participant’s Māori proficiency.
age is the age group where each participant belongs to .
gender is the gender of each participant.
ethnicity is categorized into binary answers, either Māori (M) or non Māori (non M).
education is each participant’s highest level of education.
children is each participant’s answer to the question whether they have had any children who have attended preschool or primary school in New Zealand in the past five years.
maoriList is each participant’s basic knowledge of Māori (with a scale ranging from 0 to 9).
place is each participant’s current place of living (categorized into binary classification, either North or South Island in New Zealand).
duration is each participant’s time duration of living in their current place (categorized into binary classification, long: > 10 years and short: =< 10 years).
firstLang is each participant’s first language.
firstLangCountry is the country where each participant learned their first language.
anyOtherLangs is the information regarding any other languages each participant can speak.
hawaii is the binary response to the question whether participants have lived in Hawaii.
anyPolynesian is the binary response to the question whether participants know any Polynesian such as Hawaiian, Tahitian, Sāmoan, or Tongan.
whichPolynesian is the information regarding participants’ knowledge of any Polynesian languages if they knew any.
impairments is the answer to the question whether participants have a history of any speech or language impairments.
maoriExpo is each participant’s level of exposure to Māori (with a scale ranging from 0 to 10).
Freq is the frequency of real word stimulus obtained from Māori running speech corpora (Freq=0 for pseudowords).
bin is the classification of each stimulus according to their frequency of occurence in Māori running speech corpora.
score is the dictionary phonotactic score normalized by the phonemic length of each stimulus.

2.5 NMS confidence ratings across word frequency with linear mixed-effects modeling

2.5.1 Table S1, model summary

# Model for TableS1
# modelTableS1 <- lmer(enteredResponse ~ c.(length)*c.(score) + type*bin + (1 + c.(length)*c.(score) + type*bin|workerId) + (1|word), control=lmerControl(optimizer="bobyqa"), dataExp1)
# saveRDS(modelTableS1, file = "modelTableS1.rds")
modelTableS1 <- readRDS("./modelTableS1.rds")
kable(xtable(summary(modelTableS1)$coef), digits=3, caption="Table S1: Linear mixed-effects model of NMS confidence ratings across word frequency. All numeric variables in this model are centered.")

Table S1: Linear mixed-effects model of NMS confidence ratings across word frequency. All numeric variables in this model are centered.
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.913	0.058	195.849	49.916	0.000
c.(length)	0.000	0.009	169.408	0.008	0.994
c.(score)	0.337	0.056	257.974	6.013	0.000
typereal	0.550	0.071	325.344	7.740	0.000
bin	-0.017	0.012	972.574	-1.387	0.166
c.(length):c.(score)	0.061	0.014	660.395	4.532	0.000
typereal:bin	-0.041	0.018	483.709	-2.261	0.024

2.5.2 Fig. S2, effect plots

Fig. S2: Interaction effect plots: Fig. S2a (left) shows the interaction between frequency bin and lexicality (real words vs. nonwords); Fig. S2b (right) shows the interaction between phonotactic score and length of stimuli .

2.6 Code for plotting Fig. 1

Fig. 1: Mean well-formedness ratings for real words and nonwords by frequency bin. Bin1 contains the most frequent words and Bin5 consists of the least frequent words. Horizontal lines show the model effect estimates for real words vs. nonwords across all frequencies.

2.7 Mean rating and phonotactic score for each stimulus per frequency bin

Fig. S3: Mean rating vs. phonotactic score for each stimulus for real words and nonwords by frequency bin.

2.8 Rematching stimuli pairs

During the analysis of Exp1, we found an overall bias towards higher phonotactic scores of Māori words and lower phonotactic score of Māori-like nonwords (see Fig. S3). This pattern is caused by a lack of nonwords which phonotactically matched the randomly selected words from the Māori RS corpora. To solve this problem, nonwords and words are rematched across participants by finding a phonotactically matched word with the lowest absolute difference for each nonword. Among the rematched pairs, only those whose absolute difference is below 0.3 are kept. In the initial dataset, there are 200 pairs in each bin. After rematching stimuli, the number of pairs in each bin is reduced as follows: 71 (Bin1), 52 (Bin2 and Bin3), 53 (Bin4), 56 (Bin5).

2.8.1 Rematched pairs: mean rating and phonotactic score for each stimulus per frequency bin

Fig. S4: Mean rating and phonotactic score for each stimulus per frequency bin (rematched pairs).

2.8.2 NMS confidence ratings with linear mixed-effects model (rematched pairs)

# Model for Table S2
# modelTableS2 <- lmer(enteredResponse ~ type + (1 + type|workerId) + (1|word), control=lmerControl(optimizer="bobyqa"), dataRematched) # saveRDS(modelTableS2, file = "modelTableS2.rds")
modelTableS2 <- readRDS("./modelTableS2.rds")
kable(xtable(summary(modelTableS2)$coef), digits=3, caption="Table S2: Linear mixed-effects model of NMS confidence ratings (for rematched pairs).")

Table S2: Linear mixed-effects model of NMS confidence ratings (for rematched pairs).
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.809	0.055	172.783	50.932	0
typereal	0.410	0.054	367.196	7.546	0

Within each frequency bin containing the rematched pairs, the distinction between nonwords and words is statistically significant in separate linear mixed-effects models for each bin: NMS give higher ratings to words than nonwords across all the five bins. When the ratings of all the bins are modeled in a single linear mixed-effects model, the distinction between words and nonwords is the only significant predictor; other predictors such as length, phonotactic score, and bin are systematically removed due to a lack of convergence or statistical insignificance. This result demonstrates that NMS’ ability to distinguish between words and nonwords is not an artefact of their phonotactic knowledge.

3 Experiment 2

3.1 Stimuli

The stimulus materials for Exp2 (well-formedness task), comprise of a list of 240 or 320 Māori-like nonwords generated from a trigram model using a pseudoword generator (34). About 6,000 pseudowords (1,000 pseudowords for each phoneme length of pseudowords ranging from length 3 to length 8) were generated by using a Māori dictionary (35) and two running speech (RS) corpora (32,33). Nonwords are chosen by decorrelating their phonotactic scores. The phonotactic scores of nonwords are computed using a trigram language model obtained from a Māori dictionary (35) and token frequency from segmented two RS data (32, 33), and from unsegmented RS data. The phonotactic score of each stimulus is obtained by summing log-transformed transitional probabilities which better reflect frequency distributions than raw probabilities (36) and then is normalized by length. After decorrelating the three types of phonotactic scores, 240 stimuli are chosen for a list of shorter phoneme length (3 and 4) and 320 stimuli for a list of longer phoneme length (5, 6, 7 and 8). In total, there are six lists with either 240 or 320 nonwords of the same phoneme length which are presented in a different random order. The experiment is designed this way in order not to include any additional influence from the length of stimuli. The list of stimuli used in Exp2 can be found in “StimuliExp2.txt”.

3.2 Participants

In Exp2, there are three groups of participants: Fluent Māori speakers (MS), non-Māori-speaking speakers of New Zealand English (NMS), and non-Māori-speaking speakers of American English (US). MS and NMS were recruited online by Facebook Ads and US were recruited online by Amazon Mechanical Turk. Participants are adults (18 years or older) who did not previously study linguistics. Most of NMS speak New Zealand English as their first language but 12 participants who did not learn their English in New Zealand but have been living there for at least 10 years are regarded as NMS. On the other hand, 10 participants who learned English outside New Zealand but currently live in New Zealand for less than ten years are discarded. All US speak American English as their first language and one participant who learned English outside the United States is discarded.

In addition to participants’ first language, multiple criteria are applied to detect unusable participants. For MS, two participants whose self-reported proficiency level of Māori corresponding to either “no more than a few words or phrases” or “not very well” in their post-questionnaire (below the score of 6) are discarded. For NMS, 14 participants who indicated their proficiency level of Māori as “fairly well”, “well”, or “very well” (above the score of 4) are removed. Among them, the data of five participants whose proficiency level of Māori corresponds to at least “fairly well” (equal to or above the score of 6) are imported to MS. For US, 14 participants who reported a certain proficiency level of Māori and six participants with some basic knowledge of Māori are discarded. For NMS and US, we also discard eight participants who have lived in Hawaii and have some knowledge of any other Polynesian languages such as Hawaiian, Tahitian, Tongan, and Samoan, since any knowledge of those languages belonging to the same language family as Māori may influence participants’ well-formedness judgements. However, these criteria are not applied to MS. Among all groups, five participants who reported a history of any speech or language impairments are removed.

The experiment setting for MS and NMS in New Zealand allows us to detect participants using the same browser and within each group, two participants are detected. However, after examining their data, these participants are kept as they do not look suspicious and can be identified as different participants based on their responses to the post-questionnaire. Furthermore, due to a technical error during the experiment, the data of two participants who rated too small or large number of stimuli are discarded.

After removing unusable participants, there are 40 MS, 113 NMS and 94 US. At the initial stage of filtering, 140 participants who did not complete the entire task in Experiment 2 are removed. We also filter out participants who rated items with phoneme length longer than 8 (i.e. 9 and 10 in MS and 9 in NMS) while running the experiment. This decision was made due to a very high rate of uncompleted experiment and the difficulty of recruiting participants for longer lengths such as 9 and 10. We also look at the pattern of participants’ response variability within each group and discard one participant in MS, four participants in NMS, and one participant in US whose standard deviation of responses falls below two times the standard deviation below the mean within each group and discard one participant in NMS whose median of reaction time per stimulus is shorter than two times the standard deviation below the mean within the group.

# Loading the data to filter out participants:
dataNotFiltered <- read.delim("./dataNotFilteredExp2.txt", sep ="\t", header = TRUE)

# Part 1: Removing unusable MS participants
dataMS <- dataNotFiltered[dataNotFiltered$group=="MS",]

# Remove one participant who did 285 items instead of 320 (length 5)
nbResponse <- aggregate(dataMS$enteredResponse, by=list(dataMS$workerId),  FUN=length)
rmParticipant1 <- nbResponse[!nbResponse$x %in% c(320,240),]$Group.1 #634726494EE0F397CE0F525B97FA6B76
dataMS <- dataMS[!dataMS$workerId %in% rmParticipant1,]

# Remove two participants whose speakMaori or compMaori is below 3
rmParticipant2 <- unique(dataMS[dataMS$speakMaori <3 | dataMS$compMaori < 3,]$workerId) 
#83D25CDFA45FC1A34F4A4176EC7626B3 B193DE3B787FEC86E6CCB3F82F9812E9
dataMS <- dataMS[!dataMS$workerId %in% rmParticipant2,]

# Remove one participant with language impairments
rmParticipant3 <- unique(dataMS[dataMS$impairments=="Yes",]$workerId) #6C7F9A4676E026CD0E3056D8DA19E507
dataMS <- dataMS[!dataMS$workerId %in% rmParticipant3,]

# Detect participants whose median reactionTime is shorter than 2*SD below the mean of all MS
median_RT <- aggregate(dataMS$reactionTime, by=list(dataMS$workerId), median)
names(median_RT) <- c("workerId","median");dataMS <- merge(dataMS,median_RT,by="workerId")
cut <- mean(median_RT$median)-2*sd(median_RT$median)
# median_RT[!median_RT$median > cut,]$workerId # None detected!

# Remove one participant whose pattern of responses (SD) is below 2SD of the mean of all MS participants
SD <- aggregate(dataMS$enteredResponse, by=list(dataMS$workerId), sd)
cut <- mean(SD$x)-2*sd(SD$x)
rmParticipant4 <- SD[!SD$x > cut,]$Group.1 #B33F8EB38F232BB78667404E1D96025B
dataMS <- dataMS[!dataMS$workerId %in% rmParticipant4,]

# Check the total number of usable MS participants
# length(unique(dataMS$workerId)) #40

# Part 2: Removing unusable NMS participants
dataNMS <- dataNotFiltered[dataNotFiltered$group=="NMS",]

# Remove one participant who did 436 items instead of 240 (length 4)
nbResponse <- aggregate(dataNMS$enteredResponse, by=list(dataNMS$workerId),  FUN=length)
rmParticipant5 <- nbResponse[!nbResponse$x %in% c(320,240),]$Group.1 # A7017E9F3DD1CC6778A3BC47958CA9B7
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant5,]

# Remove fourteen participants whose speakMaori or compMaori is at least (equal to or above) 3
rmParticipant6 <- unique(dataNMS[dataNMS$speakMaori >= 3 | dataNMS$compMaori >= 3,]$workerId)  # 04210C8909800F4041D775C8AE24720D 1E95A51880B3908528596F1CDC142382 1F90388DD2A6052379B3400E7E075B7B 2AABDB6A588C0A38D4D18DCCE2E411E0 3116B3E848662645051AE5AAA5D5E696 349856A9453BF2B1F7A026618E5F43D5 42564FDD3FE2B481B37DE8120E0BBB7C 5DBE9FB11F0E1FF4E5DA1149581576D0 90F93AC5E06AE66B73BFF3B46023FE3C A4B98DB8F5C9298E9FBF865FEB782486 B33F8EB38F232BB78667404E1D96025B C6243B5C4607EF24FD7F4D70B4CD0F41 C6C55B2283D35484E19BE5F4283F64D1 EB57DF86D41FFA1AD74E282B72D82856
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant6,]

# Remove ten participants who did not learn their English in NZ and have been living in their current location in NZ for less than ten years (duration == "short")
summaryNMSWorkerId <- unique(dataNMS[,c("workerId","firstLangCountry","place","duration")])
EngNotInNZ <- summaryNMSWorkerId[!summaryNMSWorkerId$firstLangCountry=="NZ",]
rmParticipant7 <- unique(EngNotInNZ[EngNotInNZ$duration=="short",]$workerId) # 21C1D24B57F6157A628A04DC5A519135 2AB61EA1EEBB7193A3A178D5B819225E 37A91EA9A4A92600B4ECEA239DC5D149 422774F5956A4A43258293A27B497995 4894A6F7674ECBFB304BCA4D9FB11B05 90FBA7311D5EB571D3C88425E4D371C0 E4E762BA8ADADA1CB0D943F8131F09B1 E862C480A8CE650797BE9D7309E52E15 FD2E69EFA7B5138BA6CC0BA31A10BFF0 FFA73BE62F1F21F94F35BC49A9B74838
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant7,]

#Remove one participant who has lived in Hawaii
rmParticipant8 <- unique(dataNMS[dataNMS$hawaii=="Yes",]$workerId) #6C3FBC26E49853CA97CFEB1DC3725C94
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant8,]

# Remove four participants who know any other Polynesian languages
rmParticipant9 <- unique(dataNMS[dataNMS$anyPolynesian=="Yes",]$workerId) # 84B5591229AA4FDE2C154B7873BCCC1E 905775C26C73AF3648FDAF216C1C836B B64C60A9F8121A22B1431FD012FE1BC1 EA6C551EB61BD1B4A3907ADCA5AC8A8C
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant9,]

# Remove three participants with language impairments
rmParticipant10 <- unique(dataNMS[dataNMS$impairments=="Yes",]$workerId) # 080658104D5BE0B7E5FDEDA8BCE32CED 504C5E7063935EB524BEC50FE180D24A CAA87C53B60F941BE5F4B3F4093B33D6
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant10,]

#Remove one participant whose median reactionTime is shorter than 2*SD below the mean of all NMS
median_RT <- aggregate(dataNMS$reactionTime, by=list(dataNMS$workerId), median)
names(median_RT) <- c("workerId","median");dataNMS <- merge(dataNMS,median_RT,by="workerId")
cut <- mean(median_RT$median)-2*sd(median_RT$median)
rmParticipant11 <- median_RT[!median_RT$median > cut,]$workerId # 0CC130754E535B995C330A62C70ABB29
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant11,]

# Remove four participants whose pattern of responses (SD) is below 2SD of the mean of all NMS participants
SD <- aggregate(dataNMS$enteredResponse, by=list(dataNMS$workerId), sd)
cut <- mean(SD$x)-2*sd(SD$x)
rmParticipant12 <- SD[!SD$x > cut,]$Group.1 # 23F8C303E5981DC46B3998C680DF6DFE 3ADCE0E04FE200BA98BC5C3015AF9092 90E5DE278473C80C5C9B1F50642DA2D5 CA1853385689F12642034089CDBFBAE3
dataNMS <- dataNMS[!dataNMS$workerId %in% rmParticipant12,]

# Check the total number of usable NMS participants
# length(unique(dataNMS$workerId)) #113

# Part3: Removing unusable US participants
dataUS <- dataNotFiltered[dataNotFiltered$group=="US",]

# Remove one participant who did 248 items instead of 240 (length 3)
nbResponse <- aggregate(dataUS$enteredResponse, by=list(dataUS$workerId),  FUN=length)
rmParticipant13 <- nbResponse[!nbResponse$x %in% c(320,240),]$Group.1 # A9692Y27LBXT9
dataUS <- dataUS[!dataUS$workerId %in% rmParticipant13,]

# Remove fourteen participants whose speakMaori or compMaori is above 0
rmParticipant14 <- unique(dataUS[dataUS$speakMaori > 0 | dataUS$compMaori > 0,]$workerId)  # A1AWHHBTJF1XCH A1L92Y6VBTRFP5 A1YAVTFZ9BULVV A28AX4H70DPKKK A2AMI7BVALOCJP A2DVRTL0JB0GT0 A2N0C5I6ZHNWV A2O5J0420C1UPC A2O762LZEZN3JC A4T642D9Z2UYC  AGVIHLR1DX7    AMKEGABQMZYIK  AMW2XLD9443OH  AWJUGWPCUGKEG 
dataUS <- dataUS[!dataUS$workerId %in% rmParticipant14,]

# Remove six participants whose maoriList is above 0
rmParticipant15 <- unique(dataUS[dataUS$maoriList > 0,]$workerId) # A19R7FSNDW6WRK A21KGSK2BDVWOA A2XOYCKJJ4PYZA A3ET9X8TXMGXDI A3V8C77FN4FLUA AP4FDDWBJW47O 
dataUS <- dataUS[!dataUS$workerId %in% rmParticipant15,]

# Remove one participant who did not learn English in the US
rmParticipant16 <- unique(dataUS[!dataUS$firstLangCountry=="US",]$workerId) # A33MBBGYASZPKN
dataUS <- dataUS[!dataUS$workerId %in% rmParticipant16,]

#Remove three participants who have been to Hawaii
rmParticipant17 <- unique(dataUS[dataUS$hawaii=="Yes",]$workerId) # A1QLYBRMIULAYC A3L0XKGZL9P2ZC ADY44EV3GCW85
dataUS <- dataUS[!dataUS$workerId %in% rmParticipant17,]

#Remove one participant with language impairments
rmParticipant18 <- unique(dataUS[dataUS$impairments=="Yes",]$workerId) # A1NUKP1UCT2R2R
dataUS <- dataUS[!dataUS$workerId %in% rmParticipant18,]

# Detect participants whose median reactionTime is shorter than 2*SD below the mean of all US
median_RT <- aggregate(dataUS$reactionTime, by=list(dataUS$workerId), median)
names(median_RT) <- c("workerId","median");dataUS <- merge(dataUS,median_RT,by="workerId")
cut <- mean(median_RT$median)-2*sd(median_RT$median)
# median_RT[!median_RT$median > cut,]$workerId # None detected!

# Remove one participant whose pattern of responses (SD) is below 2SD of the mean of all US participants
SD <- aggregate(dataUS$enteredResponse, by=list(dataUS$workerId), sd)
cut <- mean(SD$x)-2*sd(SD$x)
rmParticipant19 <- SD[!SD$x > cut,]$Group.1 # A1UHNDB2EQ8TK0
dataUS <- dataUS[!dataUS$workerId %in% rmParticipant19,]

# Check the total number of usable US participants
# length(unique(dataUS$workerId)) #94
dataExp2 <- rbind.fill(dataMS, dataNMS, dataUS)

3.3 Overview of participants’ profile

Regarding participants’ gender, there are substantially more female than male participants in MS and NMS while the gender distribution is quite balanced in US. The distribution of participants’ age is also particularly disproportionate in MS and NMS, exhibiting more participants in younger groups. Regarding participants’ highest level of education, there are more high school graduates than undergraduates and graduates in MS whereas there are more undergraduates in NMS and US. We only asked MS and NMS to provide their geographical information in New Zealand and self-report their level of exposure to Māori in daily life. There are more participants from the North Island in both MS and NMS due to a large number of population in the North Island. The level of exposure to Māori ranges from 2 (“less than once a year”) to 10 (“multiple times a day”) and the distribution of participants’ level of exposure reveals that most of MS are very frequently exposed to Māori in daily life and only a very small number of both MS and NMS are exposed to Māori less than once a year.

Fig. S5: Participants’ basic Māori knowledge and proficiency in Exp2.

Fig. S6: Overview of participants’ profile in Exp2.

Fig. S7: Overview of participants’ profile in Exp2.

3.4 Dataset structure

The data is structured as follows:

workerId is the unique ID for each participant.
enteredResponse is the wellformedness rating for each stimulus.
reactionTime is the reaction time for each rating (second).
word is the stimulus used for the rating.
speakMaori is each participant’s answer to the question how well they can speak Māori (with a scale ranging from 0 to 5).
compMaori is each participant’s answer to the question how well they can understand/read Māori (with a scale ranging from 0 to 5).
maoriProf is the sum of quantified response for speakMaori and compMaori, which refers to the level of each participant’s Māori proficiency.
age is the age group where each participant belongs to .
gender is the gender of each participant.
ethnicity is categorized into binary answers, either Māori (M) or non Māori (non M).
education is each participant’s highest level of education.
children is each participant’s answer to the question whether they have had any children who have attended preschool or primary school in New Zealand in the past five years.
maoriList is each participant’s basic knowledge of Māori (with a scale ranging from 0 to 9).
place is each participant’s current place of living (categorized into binary classification, either North or South Island in New Zealand).
duration is each participant’s time duration of living in their current place (categorized into binary classification, long: > 10 years and short: =< 10 years).
firstLang is each participant’s first language.
firstLangCountry is the country where each participant learned their first language.
anyOtherLangs is the information regarding any other languages each participant can speak.
hawaii is the binary response to the question whether participants have lived in Hawaii.
anyPolynesian is the binary response to the question whether participants know any Polynesian such as Hawaiian, Tahitian, Sāmoan, or Tongan.
whichPolynesian is the information regarding participants’ knowledge of any Polynesian languages if they knew any.
impairments is the answer to the question whether participants have a history of any speech or language impairments.
maoriExpo is each participant’s level of exposure to Māori (with a scale ranging from 0 to 10).
group is the classification of participants according to their fluency of Māori and/or exposure to Māori.
nz is the binary response to the question whether participants have ever lived in New Zealand.
median is the median of each participant’s reaction time.
scoreDic is the dictionary phonotactic score normalized by the phonemic length of each stimulus.
scoreRs is the running speech phonotactic score normalized by the phonemic length of each stimulus.
scoreUnseg is the unsegmented running speech phonotactic score normalized by the phonemic length of each stimulus - 2.
scoreDictNze is the score of the phonotactics derived from the dictionary of Māori words in New Zealand English.
scoreSmall is the score of the phonotactics derived from NMS’ small active lexicon of Māori (comprising of 121 common loanwords that most NMS can identify as Māori words in our previous study plus 55 urban placenames).
scoreFunc is the score of the phonotactics generalized over a list of function words.
scoreEng is the score of the English phonotactics obtained from the English lexical database CELEX.
scoreCV is the word shape score obtained from a trigram language model built by identifying each segment as a consonant, vowel or long vowel and calculating probabilities over sequences of those categories.
scorePosLength is the position-length specific probability of phonemes obtained from a unigram language model.
scoreDictNew is the variant phonotactice score based on the dictionary training set; statistics for trigrams, bigrams, and unigrams are jointly included.
scoreRsNew is the variant phonotactice score based on the segmented running speech training set; statistics for trigrams, bigrams, and unigrams are jointly included.
scoreUnsegNew is the variant phonotactice score based on the unsegmented running speech training set; statistics for trigrams, bigrams, and unigrams are jointly included.
scoreDictNoMacron is the dictionary phonotactic score without the distinction between short and long vowels marked with macrons.
scoreRsNoMacron is the running speech phonotactic score without the distinction between short and long vowels marked with macrons.
scoreUnsegNoMacron is the unsegmented running speech phonotactic score without the distinction between short and long vowels marked with macrons.

3.5 Number of participants per length within each group

Table S3: Number of participants per length within each group
length	MS	NMS	US
3	8	18	16
4	5	17	13
5	6	20	16
6	7	21	16
7	9	18	14
8	5	19	19

3.6 Well-formedness ratings by participant group (linear mixed-effects model)

# Model for Table S4
dataExp2$group <- relevel(dataExp2$group, ref="NMS")
# modelTableS4 <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1 + group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelTableS4, file = "modelTableS4.rds")
modelTableS4 <- readRDS("./modelTableS4.rds")
kable(xtable(summary(modelTableS4)$coef), digits=3, caption="Table S4: Mixed effects model summary for well-formedness ratings by participant group. All numeric variables in this model are centered.")

Table S4: Mixed effects model summary for well-formedness ratings by participant group. All numeric variables in this model are centered.
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.798	0.052	279.354	53.625	0.000
c.(scoreDict)	0.640	0.069	530.620	9.312	0.000
groupMS	0.006	0.099	246.339	0.057	0.955
groupUS	0.327	0.076	256.044	4.315	0.000
c.(scoreRs)	0.144	0.029	1303.637	4.997	0.000
c.(scoreUnseg)	-0.161	0.026	964.703	-6.263	0.000
c.(scoreDict):groupMS	0.025	0.109	242.850	0.227	0.821
c.(scoreDict):groupUS	-0.683	0.089	322.623	-7.680	0.000
groupMS:c.(scoreRs)	0.000	0.032	307.581	0.015	0.988
groupUS:c.(scoreRs)	-0.091	0.031	676.398	-2.929	0.004
groupMS:c.(scoreUnseg)	0.062	0.031	233.161	1.981	0.049
groupUS:c.(scoreUnseg)	0.132	0.029	457.585	4.563	0.000

3.7 Code for plotting Fig. 2

Fig. 2: Interaction between phonotactic scores and participant groups. The range of phonotactic score is represented on the x-axis and the range of predicted rating is represented on the y-axis.

3.8 Post hoc exploration

3.8.1 Monte Carlo analyses

# Example of R code to randomly sample 1000 words from the Māori dictionary
 N <- 1000; m <- 1000
 set.seed(1234)
 for(i in 1:N){
 randomWords <- sample(maoriDictionary, m)
 randomWordsTri <- orthoCoding(randomWords,g=c(3)) 
 randomWordsTri <- gsub("_"," ",randomWordsTri)
 outputFile <- paste0("randomization_", i, ".txt")
 write.table(randomWordsTri, outputFile, row.names = FALSE, col.names=FALSE, quote=FALSE)
 }  

# Example of R code to run linear mixed-effects models with 1000 samples
listLmerfit <- list()
for(i in 1:1000){
  lmerfit<- lmer(enteredResponse ~ c.(score) + (1 + c.(score)|workerId) + (1|word), data=files[[i]])
  listLmerfit[[i]] <- coef(summary(lmerfit))[2,4];message('Regression model of', i,'\n')
}
listLmerfitFinal <- unlist(listLmerfit)

3.8.2 Code for plotting Fig. 3

Fig. 3: Monte Carlo simulations with 1,000 random samples over 15 dictionary sizes.

3.8.3 Code for plotting Fig. 6

Figure 6: Density plot of t-values for frequency-weighted random samples

3.8.4 Additional post hoc statistical analyses

The following predictors are added individually to the linear mixed-effects model presented in Table S4 and Fig. 2.

3.8.4.1 Phonotactics derived from the dictionary of Māori words in New Zealand English (Macalister, 2005)

# (a) Phonotactics derived from the dictionary of Māori words in New Zealand English (Macalister, 2005)
# modelMāoriBorrowings <- lmer(enteredResponse ~ c.(scoreDictNze)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreDictNze) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelMāoriBorrowings, file = "modelMāoriBorrowings.rds")
modelMāoriBorrowings <- readRDS("./modelMāoriBorrowings.rds")
kable(xtable(summary(modelMāoriBorrowings)$coef), digits=3, caption="Table S5: Linear mixed-effects model of well-formedness ratings including the phonotactics derived from the dictionary of Māori words in New Zealand English")

Table S5: Linear mixed-effects model of well-formedness ratings including the phonotactics derived from the dictionary of Māori words in New Zealand English
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.798	0.052	279.213	53.627	0.000
c.(scoreDictNze)	0.434	0.162	1356.624	2.678	0.008
groupMS	0.005	0.099	246.335	0.047	0.962
groupUS	0.326	0.076	256.062	4.311	0.000
c.(scoreDict)	0.483	0.094	742.091	5.155	0.000
c.(scoreRs)	0.132	0.029	1303.639	4.542	0.000
c.(scoreUnseg)	-0.157	0.026	966.223	-6.093	0.000
c.(scoreDictNze):groupMS	-0.169	0.176	324.977	-0.963	0.336
c.(scoreDictNze):groupUS	-0.240	0.173	727.496	-1.382	0.167
groupMS:c.(scoreDict)	0.088	0.135	255.346	0.649	0.517
groupUS:c.(scoreDict)	-0.597	0.115	391.737	-5.193	0.000
groupMS:c.(scoreRs)	0.005	0.032	307.499	0.142	0.887
groupUS:c.(scoreRs)	-0.084	0.031	679.990	-2.686	0.007
groupMS:c.(scoreUnseg)	0.061	0.031	234.060	1.950	0.052
groupUS:c.(scoreUnseg)	0.130	0.029	460.185	4.476	0.000

3.8.4.2 Phonotactics derived from NMS’ small active lexicon of Māori

# (b) Phonotactics derived from NMS’ small active lexicon of Māori (comprising of 121 common loanwords that most NMS can identify as Māori words in our previous study plus 55 urban placenames)
# modelNMSLexicon <- lmer(enteredResponse ~ c.(scoreSmall)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreSmall) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelNMSLexicon, file = "modelNMSLexicon.rds")
modelNMSLexicon <- readRDS("./modelNMSLexicon.rds")
kable(xtable(summary(modelNMSLexicon)$coef), digits=3, caption="Table S6: Linear mixed-effects model of well-formedness ratings including the phonotactics derived from NMS’ small active lexicon of Māori")

Table S6: Linear mixed-effects model of well-formedness ratings including the phonotactics derived from NMS’ small active lexicon of Māori
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.798	0.052	279.286	53.636	0.000
c.(scoreSmall)	0.436	0.235	1411.175	1.858	0.063
groupMS	0.005	0.099	246.333	0.055	0.956
groupUS	0.327	0.076	256.037	4.318	0.000
c.(scoreDict)	0.587	0.076	581.840	7.699	0.000
c.(scoreRs)	0.136	0.029	1298.057	4.674	0.000
c.(scoreUnseg)	-0.160	0.026	963.925	-6.245	0.000
c.(scoreSmall):groupMS	-0.352	0.244	346.084	-1.444	0.150
c.(scoreSmall):groupUS	-0.362	0.247	801.583	-1.469	0.142
groupMS:c.(scoreDict)	0.066	0.118	245.848	0.563	0.574
groupUS:c.(scoreDict)	-0.641	0.097	338.684	-6.577	0.000
groupMS:c.(scoreRs)	0.007	0.032	305.144	0.217	0.828
groupUS:c.(scoreRs)	-0.085	0.031	672.445	-2.694	0.007
groupMS:c.(scoreUnseg)	0.061	0.031	232.214	1.967	0.050
groupUS:c.(scoreUnseg)	0.132	0.029	457.164	4.556	0.000

3.8.4.3 Phonotactics generalized over a list of function words

# (c) Phonotactics generalized over a list of function words
# modelFunction <- lmer(enteredResponse ~ c.(scoreFunc)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreFunc) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelFunction, file = "modelFunction.rds")
modelFunction <- readRDS("./modelFunction.rds")
kable(xtable(summary(modelFunction)$coef), digits=3, caption="Table S7: Linear mixed-effects model of well-formedness ratings including the phonotactics generalized over a list of function words")

Table S7: Linear mixed-effects model of well-formedness ratings including the phonotactics generalized over a list of function words
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.798	0.052	279.340	53.651	0.000
c.(scoreFunc)	0.271	0.165	1377.414	1.642	0.101
groupMS	0.005	0.099	246.359	0.053	0.958
groupUS	0.327	0.076	256.071	4.314	0.000
c.(scoreDict)	0.629	0.069	526.485	9.073	0.000
c.(scoreRs)	0.134	0.029	1312.144	4.567	0.000
c.(scoreUnseg)	-0.163	0.026	969.464	-6.363	0.000
c.(scoreFunc):groupMS	-0.010	0.171	322.338	-0.059	0.953
c.(scoreFunc):groupUS	-0.035	0.174	764.269	-0.200	0.842
groupMS:c.(scoreDict)	0.026	0.110	241.969	0.235	0.814
groupUS:c.(scoreDict)	-0.681	0.090	320.894	-7.573	0.000
groupMS:c.(scoreRs)	0.001	0.032	307.824	0.021	0.983
groupUS:c.(scoreRs)	-0.089	0.031	686.135	-2.842	0.005
groupMS:c.(scoreUnseg)	0.062	0.031	233.516	1.975	0.049
groupUS:c.(scoreUnseg)	0.131	0.029	460.344	4.547	0.000

3.8.4.4 English phonotactics obtained from the English lexical database CELEX (Baayen, Piepen-brock & van H, 1993)

# (d) English phonotactics obtained from the English lexical database CELEX (Baayen, Piepen-brock & van H, 1993)
# modelEng <- lmer(enteredResponse ~ c.(scoreEng)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreEng) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelEng, file = "modelEng.rds")
modelEng <- readRDS("./modelEng.rds")
kable(xtable(summary(modelEng)$coef), digits=3, caption="Table S8: Linear mixed-effects model of well-formedness ratings including the English phonotactics obtained from the English lexical database CELEX")

Table S8: Linear mixed-effects model of well-formedness ratings including the English phonotactics obtained from the English lexical database CELEX
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.798	0.052	279.320	53.573	0.000
c.(scoreEng)	-0.032	0.019	1319.566	-1.751	0.080
groupMS	0.006	0.099	246.371	0.060	0.952
groupUS	0.328	0.076	256.012	4.333	0.000
c.(scoreDict)	0.641	0.069	530.596	9.339	0.000
c.(scoreRs)	0.143	0.029	1304.275	4.976	0.000
c.(scoreUnseg)	-0.161	0.026	962.520	-6.253	0.000
c.(scoreEng):groupMS	0.005	0.021	319.815	0.232	0.816
c.(scoreEng):groupUS	-0.017	0.020	697.304	-0.848	0.397
groupMS:c.(scoreDict)	0.024	0.109	243.111	0.218	0.828
groupUS:c.(scoreDict)	-0.683	0.089	322.407	-7.681	0.000
groupMS:c.(scoreRs)	0.000	0.032	307.677	-0.012	0.990
groupUS:c.(scoreRs)	-0.093	0.031	675.526	-3.006	0.003
groupMS:c.(scoreUnseg)	0.062	0.031	232.117	1.986	0.048
groupUS:c.(scoreUnseg)	0.133	0.029	455.149	4.613	0.000

3.8.4.5 Word shape score

# (e) Word shape score obtained from a trigram language model built by identifying each segment as a consonant, vowel or long vowel and calculating probabilities over sequences of those categories
# modelWordshape <- lmer(enteredResponse ~ c.(scoreCV)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreCV) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelWordshape, file = "modelWordshape.rds")
modelWordshape <- readRDS("./modelWordshape.rds")
kable(xtable(summary(modelWordshape)$coef), digits=3, caption="Table S9: Linear mixed-effects model of well-formedness ratings including word shape scores")

Table S9: Linear mixed-effects model of well-formedness ratings including word shape scores
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.785	0.053	280.210	52.977	0.000
c.(scoreCV)	-0.019	0.026	555.046	-0.713	0.476
groupMS	-0.011	0.100	246.347	-0.115	0.908
groupUS	0.323	0.076	255.726	4.232	0.000
c.(scoreDict)	0.671	0.070	973.649	9.629	0.000
c.(scoreRs)	0.139	0.029	1300.170	4.841	0.000
c.(scoreUnseg)	-0.159	0.026	1005.330	-6.169	0.000
c.(scoreCV):groupMS	-0.038	0.042	253.720	-0.920	0.358
c.(scoreCV):groupUS	-0.059	0.034	333.169	-1.748	0.081
groupMS:c.(scoreDict)	0.106	0.092	271.120	1.161	0.247
groupUS:c.(scoreDict)	-0.545	0.081	464.426	-6.753	0.000
groupMS:c.(scoreRs)	-0.002	0.032	305.368	-0.077	0.939
groupUS:c.(scoreRs)	-0.094	0.031	650.843	-3.080	0.002
groupMS:c.(scoreUnseg)	0.052	0.030	234.587	1.722	0.086
groupUS:c.(scoreUnseg)	0.105	0.028	465.731	3.718	0.000

3.8.4.6 Position-length specific probabilities of phonemes

# (f) Position-length specific probabilities of phonemes obtained from a unigram language model
# modelPosLength <- lmer(enteredResponse ~ c.(scorePosLength)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scorePosLength) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelPosLength, file = "modelPosLength.rds")
modelPosLength <- readRDS("./modelPosLength.rds")
kable(xtable(summary(modelPosLength)$coef), digits=3, caption="Table S10: Linear mixed-effects model of well-formedness ratings including position-length specific probabilities of phonemes")

Table S10: Linear mixed-effects model of well-formedness ratings including position-length specific probabilities of phonemes
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.809	0.054	276.178	52.130	0.000
c.(scorePosLength)	-0.073	0.080	482.355	-0.912	0.362
groupMS	0.021	0.102	245.116	0.202	0.840
groupUS	0.330	0.078	254.322	4.211	0.000
c.(scoreDict)	0.643	0.071	689.026	9.065	0.000
c.(scoreRs)	0.141	0.029	1260.914	4.936	0.000
c.(scoreUnseg)	-0.153	0.025	966.605	-6.100	0.000
c.(scorePosLength):groupMS	-0.136	0.122	201.490	-1.118	0.265
c.(scorePosLength):groupUS	-0.076	0.101	277.885	-0.758	0.449
groupMS:c.(scoreDict)	0.100	0.104	252.568	0.960	0.338
groupUS:c.(scoreDict)	-0.624	0.088	374.400	-7.088	0.000
groupMS:c.(scoreRs)	-0.005	0.032	296.740	-0.170	0.865
groupUS:c.(scoreRs)	-0.095	0.031	648.074	-3.071	0.002
groupMS:c.(scoreUnseg)	0.054	0.030	226.573	1.788	0.075
groupUS:c.(scoreUnseg)	0.122	0.028	456.681	4.323	0.000

3.8.4.7 Presence of macrons

# (g) Presence of 1 or more macrons
# dataExp2$macron <- FALSE
# dataExp2[grepl("ā|ē|ī|ō|ū",dataExp2$word),]$macron <- TRUE
# modelMacron <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + macron*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + macron|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelMacron, file = "modelMacron.rds")
modelMacron <- readRDS("./modelMacron.rds")
kable(xtable(summary(modelMacron)$coef), digits=3, caption="Table S11: Linear mixed-effects model of well-formedness ratings including the presence of macron(s)")

Table S11: Linear mixed-effects model of well-formedness ratings including the presence of macron(s)
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.162	0.078	438.668	27.573	0.000
c.(scoreDict)	0.869	0.067	550.471	13.063	0.000
groupMS	0.202	0.133	258.319	1.525	0.128
groupUS	0.419	0.106	318.264	3.936	0.000
c.(scoreRs)	0.114	0.027	1220.609	4.185	0.000
c.(scoreUnseg)	-0.134	0.024	879.359	-5.484	0.000
macronTRUE	0.722	0.074	536.075	9.808	0.000
c.(scoreDict):groupMS	-0.033	0.105	252.409	-0.313	0.754
c.(scoreDict):groupUS	-0.709	0.087	346.916	-8.165	0.000
groupMS:c.(scoreRs)	0.007	0.031	307.679	0.210	0.834
groupUS:c.(scoreRs)	-0.087	0.031	672.274	-2.842	0.005
groupMS:c.(scoreUnseg)	0.056	0.031	233.393	1.808	0.072
groupUS:c.(scoreUnseg)	0.124	0.029	455.002	4.346	0.000
groupMS:macronTRUE	-0.212	0.118	264.868	-1.788	0.075
groupUS:macronTRUE	-0.101	0.097	354.016	-1.044	0.297

3.8.4.8 Presence of digraphs

# (h) Presence of 1 or more digraph(s) such as 'wh' and 'ng'
dataExp2$digraph <- FALSE
dataExp2[grepl("wh|ng",dataExp2$word),]$digraph <- TRUE
# modelDigraph <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + digraph*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + digraph|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelDigraph, file = "modelDigraph.rds")
modelDigraph <- readRDS("./modelDigraph.rds")
kable(xtable(summary(modelDigraph)$coef), digits=3, caption="Table S12: Linear mixed-effects model of well-formedness ratings including the presence of digraph(s)")

Table S12: Linear mixed-effects model of well-formedness ratings including the presence of digraph(s)
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.736	0.054	295.528	50.933	0.000
c.(scoreDict)	0.643	0.069	521.557	9.376	0.000
groupMS	0.032	0.100	247.138	0.317	0.751
groupUS	0.435	0.077	259.639	5.637	0.000
c.(scoreRs)	0.153	0.029	1287.519	5.357	0.000
c.(scoreUnseg)	-0.138	0.026	974.560	-5.401	0.000
digraphTRUE	0.176	0.042	673.956	4.187	0.000
c.(scoreDict):groupMS	0.025	0.109	242.887	0.231	0.818
c.(scoreDict):groupUS	-0.687	0.088	310.433	-7.776	0.000
groupMS:c.(scoreRs)	-0.001	0.032	303.753	-0.028	0.978
groupUS:c.(scoreRs)	-0.106	0.030	620.529	-3.497	0.001
groupMS:c.(scoreUnseg)	0.053	0.031	235.953	1.705	0.090
groupUS:c.(scoreUnseg)	0.090	0.028	437.187	3.202	0.001
groupMS:digraphTRUE	-0.076	0.061	238.607	-1.250	0.213
groupUS:digraphTRUE	-0.314	0.051	345.714	-6.187	0.000

3.8.4.9 Maximum length of vowel sequence

# (i) Maximum length of vowel sequence
# Add the maximum length of vowel sequence
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū][aeiouāēīōū][aeiouāēīōū][aeiouāēīōū]","5", dataExp2$word)
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū][aeiouāēīōū][aeiouāēīōū]","4", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū][aeiouāēīōū]","3", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū]","2", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[aeiouāēīōū]","1", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[whkmrngpt]","", dataExp2$vSeq)
dataExp2$vSeq <- gsub(""," ",dataExp2$vSeq)
vSeqList <- strsplit(dataExp2$vSeq,split=" ");vSeqList1 <- sapply(vSeqList,max)
dataExp2$vSeq <- vSeqList1;dataExp2$vSeq <- as.numeric(dataExp2$vSeq)
# modelVSeq <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + c.(vSeq)*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + c.(vSeq)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelVSeq, file = "modelVSeq.rds")
modelVSeq <- readRDS("./modelVSeq.rds")
kable(xtable(summary(modelVSeq)$coef), digits=3, caption="Table S13: Linear mixed-effects model of well-formedness ratings including the maximum length of vowel sequence")

Table S13: Linear mixed-effects model of well-formedness ratings including the maximum length of vowel sequence
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.798	0.052	274.315	54.264	0.000
c.(scoreDict)	0.466	0.065	522.340	7.203	0.000
groupMS	0.005	0.098	246.253	0.053	0.958
groupUS	0.328	0.075	255.298	4.364	0.000
c.(scoreRs)	0.114	0.026	1253.653	4.303	0.000
c.(scoreUnseg)	-0.074	0.023	1092.396	-3.288	0.001
c.(vSeq)	-0.324	0.028	536.630	-11.584	0.000
c.(scoreDict):groupMS	0.127	0.103	244.729	1.234	0.218
c.(scoreDict):groupUS	-0.554	0.085	329.474	-6.552	0.000
groupMS:c.(scoreRs)	0.021	0.030	309.210	0.695	0.488
groupUS:c.(scoreRs)	-0.066	0.029	683.144	-2.251	0.025
groupMS:c.(scoreUnseg)	0.012	0.025	239.385	0.471	0.638
groupUS:c.(scoreUnseg)	0.067	0.025	554.971	2.671	0.008
groupMS:c.(vSeq)	0.180	0.045	253.257	4.049	0.000
groupUS:c.(vSeq)	0.240	0.037	339.698	6.584	0.000

3.8.4.10 Type of first segment (consonant or vowel)

# (j) Type of first segment (consonant or vowel)
dataExp2$first <- gsub("[aeiouāēīōū]","v",dataExp2$word)
dataExp2$first <- gsub("[whkmrngpt]","c",dataExp2$first)
dataExp2$first <- substr(dataExp2$first,1,1)
dataExp2$first <- as.factor(dataExp2$first)
# modelFirst <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + first*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + first|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelFirst, file = "modelFirst.rds")
modelFirst <- readRDS("./modelFirst.rds")
kable(xtable(summary(modelFirst)$coef), digits=3, caption="Table S14: Linear mixed-effects model of well-formedness ratings including the type of first segment")

Table S14: Linear mixed-effects model of well-formedness ratings including the type of first segment
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	3.014	0.056	319.154	53.366	0.000
c.(scoreDict)	0.515	0.065	543.173	7.903	0.000
groupMS	-0.063	0.104	248.565	-0.609	0.543
groupUS	0.148	0.080	269.415	1.849	0.066
c.(scoreRs)	0.110	0.027	1260.092	3.989	0.000
c.(scoreUnseg)	-0.101	0.024	1029.135	-4.212	0.000
firstv	-0.378	0.043	565.255	-8.716	0.000
c.(scoreDict):groupMS	0.108	0.103	244.910	1.049	0.295
c.(scoreDict):groupUS	-0.568	0.084	329.468	-6.745	0.000
groupMS:c.(scoreRs)	0.011	0.031	300.282	0.342	0.733
groupUS:c.(scoreRs)	-0.061	0.030	655.438	-2.030	0.043
groupMS:c.(scoreUnseg)	0.028	0.028	239.674	1.013	0.312
groupUS:c.(scoreUnseg)	0.077	0.027	499.843	2.892	0.004
groupMS:firstv	0.150	0.068	245.512	2.214	0.028
groupUS:firstv	0.322	0.056	335.226	5.783	0.000

3.8.4.11 Variant phonotactic scores including trigrams, bigrams, and unigrams

# (k) Variant phonotactic scores including trigrams, bigrams, and unigrams
# modelNewScores <- lmer(enteredResponse ~ c.(scoreDictNew)*group + c.(scoreRsNew)*group + c.(scoreUnsegNew)*group + (1 + c.(scoreDictNew) + c.(scoreRsNew) + c.(scoreUnsegNew)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelNewScores, file = "modelNewScores.rds")
modelNewScores <- readRDS("./modelNewScores.rds")
kable(xtable(summary(modelNewScores)$coef), digits=3, caption="Table S15: Linear mixed-effects model of well-formedness ratings including variant phonotactic scores with trigrams, bigrams, and unigrams.")

Table S15: Linear mixed-effects model of well-formedness ratings including variant phonotactic scores with trigrams, bigrams, and unigrams.
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.787	0.052	274.287	53.212	0.000
c.(scoreDictNew)	0.809	0.077	669.797	10.564	0.000
groupMS	-0.007	0.100	245.716	-0.068	0.946
groupUS	0.321	0.076	255.204	4.208	0.000
c.(scoreRsNew)	0.285	0.038	1262.070	7.534	0.000
c.(scoreUnsegNew)	-0.267	0.032	827.698	-8.311	0.000
c.(scoreDictNew):groupMS	-0.002	0.113	245.741	-0.018	0.986
c.(scoreDictNew):groupUS	-0.709	0.096	373.479	-7.388	0.000
groupMS:c.(scoreRsNew)	-0.071	0.043	316.985	-1.650	0.100
groupUS:c.(scoreRsNew)	-0.241	0.042	691.138	-5.724	0.000
groupMS:c.(scoreUnsegNew)	0.089	0.042	246.342	2.117	0.035
groupUS:c.(scoreUnsegNew)	0.162	0.038	442.054	4.248	0.000

3.8.4.12 Phonotactic scores without the distinction between short and long vowels marked with macrons

# (l) Phonotactic scores without the distinction between short and long vowels marked with macrons
# modelNoMacron <- lmer(enteredResponse ~ c.(scoreDictNoMacron)*group + c.(scoreRsNoMacron)*group + c.(scoreUnsegNoMacron)*group + (1 + c.(scoreDictNoMacron) + c.(scoreRsNoMacron) + c.(scoreUnsegNoMacron)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelNoMacron, file = "modelNoMacron.rds")
modelNoMacron <- readRDS("./modelNoMacron.rds")
kable(xtable(summary(modelNoMacron)$coef), digits=3, caption="Table S16: Linear mixed-effects model of well-formedness ratings including phonotactic scores without the distinction between short and long vowels marked with macrons")

Table S16: Linear mixed-effects model of well-formedness ratings including phonotactic scores without the distinction between short and long vowels marked with macrons
	Estimate	Std. Error	df	t value	Pr(>\|t\|)
(Intercept)	2.797	0.052	268.601	54.070	0.000
c.(scoreDictNoMacron)	1.180	0.082	611.118	14.460	0.000
groupMS	0.002	0.099	246.574	0.024	0.981
groupUS	0.329	0.076	254.942	4.343	0.000
c.(scoreRsNoMacron)	0.130	0.044	1241.548	2.958	0.003
c.(scoreUnsegNoMacron)	-0.310	0.039	599.160	-7.937	0.000
c.(scoreDictNoMacron):groupMS	-0.159	0.126	263.149	-1.259	0.209
c.(scoreDictNoMacron):groupUS	-0.901	0.106	388.697	-8.466	0.000
groupMS:c.(scoreRsNoMacron)	-0.012	0.051	356.790	-0.240	0.810
groupUS:c.(scoreRsNoMacron)	-0.088	0.051	775.953	-1.734	0.083
groupMS:c.(scoreUnsegNoMacron)	0.053	0.059	248.792	0.891	0.374
groupUS:c.(scoreUnsegNoMacron)	0.313	0.051	378.573	6.185	0.000

4 Post-questionnaire

How well are you able to speak Māori?
☐ Very well (I can talk about almost anything in Māori)
☐ Well (I can talk about many things in Māori)
☐ Fairly well (I can talk about some things in Māori)
☐ Not very well (I can only talk about simple/basic things in Māori)
☐ No more than a few words or phrases
☐ Not at all
How well are you able to understand/read Māori?
☐ Very well (I can understand almost anything said/written in Māori)
☐ Well (I can understand many things said/written in Māori)
☐ Fairly well (I can understand some things said/written in Māori)
☐ Not very well (I can only understand simple/basic things said/written in Māori)
☐ No more than a few words or phrases
☐ Not at all
Which age group do you belong to?
☐ 18 - 29
☐ 30 - 39
☐ 40 - 49
☐ 50 - 59
☐ +60
Please state your gender:
Please state your ethnicity:
Your highest education is:
☐ High school
☐ Undergraduate degree
☐ Graduate degree
How often do you think you are exposed to the Māori language in your daily life, by means of Māori radio, Māori TV, online media? (only relevant for fluent Māori speakers and non-fluent Māori speakers of New Zealand English)
☐ Less than once a year
☐ Less than once a month
☐ Less than once a week
☐ Less than once a day
☐ Multiple times a day
How often do you think you are exposed to Māori language in your daily life, in conversation at work, at home, in social settings? (only relevant for fluent Māori speakers and non-fluent Māori speakers of New Zealand English)
☐ Less than once a year
☐ Less than once a month
☐ Less than once a week
☐ Less than once a day
☐ Multiple times a day
In the past five years, have you had any children living with you who have attended preschool or primary school in New Zealand? (only relevant for fluent Māori speakers and non-fluent Māori speakers of New Zealand English)
☐ Yes
☐ No
Please tick all boxes that apply.
☐ I can give a mihi in Māori.
☐ I can sing a few songs in Māori.
☐ I can sing the NZ national anthem in Māori.
☐ I know how to say some basic phrases (e.g. My name is…, I’m from…) in Māori.
☐ I know how to say some commands (e.g. Sit down / Come here) in Māori.
☐ I know how to say some greetings in Māori.
☐ I know how to say some numbers in Māori.
☐ I know how to say some body parts in Māori.
☐ I know how to say some colors in Māori.
What region of New Zealand do you live in currently? (Please choose ``overseas" if you are living outside of New Zealand.) (only relevant for fluent Māori speakers and non-fluent Māori speakers of New Zealand English)
☐ Northland
☐ Auckland
☐ Waikato
☐ Bay of Plenty
☐ Gisborne
☐ Hawke’s Bay
☐ Taranaki
☐ Wanganui
☐ Manawatu
☐ Wairarapa
☐ Wellington
☐ Nelson Bays
☐ Marlborough
☐ West Coast
☐ Canterbury
☐ Timaru - Oamaru
☐ Otago
☐ Southland
☐ Overseas
How long have you been living there? (only relevant for fluent Māori speakers and non-fluent Māori speakers of New Zealand English)
Please state your first language (the language you speak/use most of your time).
What country were you living in when you first learned this language?
Please list any other languages that you can speak fluently:
Have you ever lived in Hawaii?
☐ Yes
☐ No
Have you ever lived in New Zealand (only relevant for non-fluent Māori speakers of American English)?
☐ Yes
☐ No
Do you speak/understand any Polynesian languages such as Hawaiian, Tahitian, Sāmoan, or Tongan?
☐ Yes
☐ No
If you replied yes to question 18, please state the language you know.
Do you have a history of any speech or language impairments that you are aware of?
☐ Yes
☐ No

An Adult Proto-lexicon

Participant filtering, statistical analysis, plots and tables

Yoon Mi Oh, Clay Beckner, Jen Hay, Jeanette King and Jeremy Needle

2019-03-12 09:12:43